Computational Psychology to Embed Emotions into News or Advertisements to Increase Reader Affinity
Hrishikesh Kulkarni, P Joshi, P Chande
TL;DR
This paper proposes a machine learning approach to embed emotions into news and advertisements, aiming to enhance reader engagement and personalization by understanding and mapping reader emotional traits.
Contribution
It introduces a novel computational psychology method for assessing affective value and personalizing news and ads based on reader behavior and emotional traits.
Findings
Affective value mapping improves personalization accuracy.
Algorithm effectively matches news to reader emotional profiles.
Enhanced reader satisfaction through emotion-embedded content.
Abstract
Readers take decisions about going through the complete news based on many factors. The emotional impact of the news title on reader is one of the most important factors. Cognitive ergonomics tries to strike the balance between work, product and environment with human needs and capabilities. The utmost need to integrate emotions in the news as well as advertisements cannot be denied. The idea is that news or advertisement should be able to engage the reader on emotional and behavioral platform. While achieving this objective there is need to learn about reader behavior and use computational psychology while presenting as well as writing news or advertisements. This paper based on Machine Learning, tries to map behavior of the reader with the news/advertisements and also provide inputs for affective value for building personalized news or advertisements presentations. The affective value…
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Taxonomy
TopicsColor perception and design · Neural and Behavioral Psychology Studies